Maintaining a minimum level of real time media recommendations in the absence of online friends

ABSTRACT

The present invention provides a peer-to-peer (P2P) network for providing real time media recommendations. The P2P network includes a central server interconnecting a number of peer devices. In operation, a proxy function of the central server receives media recommendations from one or more peer devices that are active and online as media presentations identified by the media recommendations are played by the peer devices. The one or more peer devices are included in a group of peer devices associated with a first peer device. The proxy function provides the media recommendations to the first peer device. An augmentation function of the central server monitors a recommendation level of the first peer device. If the recommendation level falls below a minimum recommendation level, the augmentation function augments the media recommendations provided to the first peer device to increase the recommendation level to or above the minimum recommendation level.

RELATED APPLICATIONS

This application is a Continuation-in-Part of U.S. patent applicationSer. No. 11/484,130, entitled P2P NETWORK FOR PROVIDING REAL TIME MEDIARECOMMENDATIONS, filed on Jul. 11, 2006, which is hereby incorporatedherein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to media recommendations, such as music orvideo recommendations, and more specifically relates to a peer-to-peer(P2P) network for providing real time media recommendations.

BACKGROUND OF THE INVENTION

In recent years, there has been an enormous increase in the amount ofdigital media, such as music, available online. Services such as Apple'siTunes enable users to legally purchase and download music. Otherservices such as Yahoo! Music Unlimited and RealNetwork's Rhapsodyprovide access to millions of songs for a monthly subscription fee. As aresult, music has become much more accessible to listeners worldwide.However, the increased accessibility of music has only heightened along-standing problem for the music industry, which is namely the issueof linking audiophiles with new music that matches their listeningpreferences.

Many companies, technologies, and approaches have emerged to addressthis issue of music recommendation. Some companies have taken ananalytical approach. They review various attributes of a song, such asmelody, harmony, lyrics, orchestration, vocal character, and the like,and assign a rating to each attribute. The ratings for each attributeare then assembled to create a holistic classification for the song thatis then used by a recommendation engine. The recommendation enginetypically requires that the user first identify a song that he or shelikes. The recommendation engine then suggests other songs with similarattributions. Companies using this type of approach include Pandora(http://www.pandora.com), SoundFlavor (http://www.soundflavor.com),MusicIP (http://www.musicip.com), and MongoMusic (purchased by Microsoftin 2000).

Other companies take a communal approach. They make recommendationsbased on the collective wisdom of a group of users with similar musicaltastes. These solutions first profile the listening habits of aparticular user and then search similar profiles of other users todetermine recommendations. Profiles are generally created in a varietyof ways such as looking at a user's complete collection, the playcountsof their songs, their favorite playlists, and the like. Companies usingthis technology include Last.fm (http://www.last.fm), Music Strands(http://www.musicstrands.com), WebJay (http://www.webjay.org), Mercora(http://www.mercora.com), betterPropaganda(http://www.betterpropaganda.com), Loomia (http://www.loomia.com),eMusic (http://www.emusic.com), musicmatch(http://www.mmguide.musicmatch.com), genielab (http://genielab.com/), upto 11 (http://www.upto11.net/), Napster (http://www.napster.com), andiTunes (http://www.itunes.com) with its celebrity playlists.

The problem with these traditional recommendation systems is that theyfail to consider peer influences. For example, the music to which aparticular teenager listens may be highly influenced by the musiclistened to by a group of the teenager's peers, such as his or herfriends. As such, there is a need for a music recommendation system andmethod that recommends music to a user based on the listening habits ofa peer group.

SUMMARY OF THE INVENTION

The present invention provides a peer-to-peer (P2P) network forproviding real time media recommendations. The media recommendations maybe song recommendations or video recommendations. In general, the P2Pnetwork includes a central server interconnecting a number of peerdevices. In operation, a proxy function of the central server receivesmedia recommendations from one or more peer devices that are active andonline as media presentations identified by the media recommendationsare played by the peer devices. The one or more peer devices areincluded in a group of peer devices associated with a first peer device.The proxy function provides the media recommendations to the first peerdevice. The first peer device then selects a media presentation to playfrom a group of media presentations including the media presentationsidentified by the media recommendations.

An augmentation function of the central server monitors a recommendationlevel of the first peer device. The recommendation level is indicativeof the number of media recommendations provided to the first peerdevice. If the recommendation level falls below a minimum recommendationlevel, the augmentation function augments the media recommendationsprovided to the first peer device to increase the recommendation levelto or above the minimum recommendation level.

In a first embodiment, the augmentation function augments the mediarecommendations provided to the first peer device by selecting anadditional peer device. The augmentation function then augments themedia recommendations provided to the first peer device with mediarecommendations from the additional peer device.

In a second embodiment, the augmentation function augments the mediarecommendations provided to the first peer device based on historicaldata for one of the group of peer devices that is offline or inactive.More specifically, the augmentation function identifies one of the groupof peer devices that is offline, and generates additional mediarecommendations based on a play history of the identified peer device.The augmentation function then augments the media recommendationsprovided to the first peer device with the additional mediarecommendations.

In a third embodiment, the augmentation function generates additionalmedia recommendations based on a user profile of one of the group ofpeer devices that is offline or inactive. The augmentation function thenaugments the media recommendations provided to the first peer devicewith the additional media recommendations.

In a fourth embodiment, the augmentation function generates additionalmedia recommendations based on a user profile of a user of the firstpeer device. The augmentation function then augments the recommendationprovided to the first peer device with the additional mediarecommendations.

Those skilled in the art will appreciate the scope of the presentinvention and realize additional aspects thereof after reading thefollowing detailed description of the preferred embodiments inassociation with the accompanying drawing figures.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawing figures incorporated in and forming a part ofthis specification illustrate several aspects of the invention, andtogether with the description serve to explain the principles of theinvention.

FIG. 1 illustrates a system incorporating a peer-to-peer (P2P) networkfor real time media recommendations according to one embodiment of thepresent invention;

FIG. 2 illustrates the registry of one of the peer devices in moredetail according to one embodiment of the present invention;

FIG. 3 illustrates the operation of the content identification functionof FIG. 1 according to one embodiment of the present invention;

FIG. 4 illustrates the operation of the peer device of FIG. 1 accordingto one embodiment of the present invention;

FIG. 5 illustrates the operation of the system of FIG. 1 according toone embodiment of the present invention;

FIG. 6 illustrates a system incorporating a P2P network for real timemedia recommendations according to a second embodiment of the presentinvention;

FIG. 7 illustrates the operation of the system of FIG. 6 according toone embodiment of the present invention;

FIG. 8 is a flow chart illustrating a method for automatically selectingmedia to play based on recommendations from peer devices and userpreferences according to one embodiment of the present invention;

FIG. 9 illustrates an exemplary graphical user interface (GUI) forconfiguring user preferences according to one embodiment of the presentinvention;

FIG. 10 illustrates an exemplary GUI for assigning weights to variouscategories of media content as part of configuring the user preferencesaccording to one embodiment of the present invention;

FIG. 11 illustrates an exemplary GUI for assigning weights to individualusers within a user category as part of configuring the user preferencesaccording to one embodiment of the present invention;

FIG. 12 illustrates an exemplary GUI for assigning weights to individualgenres from a genre category as part of configuring the user preferencesaccording to one embodiment of the present invention;

FIG. 13 illustrates an exemplary GUI for assigning weights to individualdecades from a decade category as part of configuring the userpreferences according to one embodiment of the present invention;

FIG. 14 illustrates an exemplary GUI for assigning weights to individualavailability types from an availability type category as part ofconfiguring the user preferences according to one embodiment of thepresent invention;

FIG. 15 illustrates an exemplary GUI displaying a playlist includingsongs from both a local music collection of a peer device andrecommended songs from other peer devices, where the songs are sorted bya score determined based on user preferences according to one embodimentof the present invention;

FIG. 16 illustrates an exemplary GUI displaying a playlist includingsongs from both a local music collection of a peer device andrecommended songs from other peer devices, where the songs are sorted byboth genre and score according to one embodiment of the presentinvention;

FIG. 17 illustrates a system incorporating a P2P network for real timemedia recommendations according to a second embodiment of the presentinvention;

FIG. 18 illustrates the operation of the central server to maintain adesired level of real time media recommendations in the absence ofactive, online friends according to a second embodiment of the presentinvention;

FIG. 19 is a block diagram of a peer device of FIG. 1 according to oneembodiment of the present invention;

FIG. 20 is a block diagram of a peer device of FIG. 6 according to oneembodiment of the present invention;

FIG. 21 is a block diagram of the central server of FIG. 6 according toone embodiment of the present invention; and

FIG. 22 is a block diagram of the central server of FIG. 17 according toone embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The embodiments set forth below represent the necessary information toenable those skilled in the art to practice the invention and illustratethe best mode of practicing the invention. Upon reading the followingdescription in light of the accompanying drawing figures, those skilledin the art will understand the concepts of the invention and willrecognize applications of these concepts not particularly addressedherein. It should be understood that these concepts and applicationsfall within the scope of the disclosure and the accompanying claims.

FIG. 1 illustrates a system 10 incorporating a peer-to-peer (P2P)network for providing real time song recommendations according to oneembodiment of the present invention. Note that while the discussionherein focuses on song recommendations for clarity and ease ofdiscussion, the present invention is equally applicable to providingrecommendations for other types of media presentations such as videopresentations, as will be apparent to one of ordinary skill in the artupon reading this disclosure. Exemplary video presentations are movies,television programs, and the like. In general, the system 10 includes anumber of peer devices 12-16 which are optionally connected to asubscription music service 18 via a network 20, which may be adistributed public network such as, but not limited to, the Internet.Note that while three peer devices 12-16 are illustrated, the presentinvention may be used with any number of two or more peer devices.

In this embodiment, the peer devices 12-16 are preferably portabledevices such as, but not limited to, portable audio players, mobiletelephones, Personal Digital Assistants (PDAs), or the like having audioplayback capabilities. However, the peer devices 12-16 may alternativelybe stationary devices such as personal computers or the like. The peerdevices 12-16 include local wireless communication interfaces (FIG. 19)communicatively coupling the peer devices 12-16 to form a P2P network.The wireless communication interfaces may provide wireless communicationaccording to, for example, one of the suite of IEEE 802.11 standards,the Bluetooth standard, or the like.

The peer device 12 includes a music player 22, a recommendation engine24, a music collection 26, and a registry 28. The music player 22 may beimplemented in software, hardware, or a combination of hardware andsoftware. In general, the music player 22 operates to play songs fromthe music collection 26. The recommendation engine 24 may be implementedin software, hardware, or a combination of hardware and software. Therecommendation engine 24 may alternatively be incorporated into themusic player 22. The music collection 26 includes any number of songsstored in one or more digital storage units such as, for example, one ormore hard-disc drives, one or more memory cards, internal Random-AccessMemory (RAM), one or more associated external digital storage devices,or the like. The registry 28 operates to store a registry entry for eachsong in the music collection 26. In general, each registry entryincludes a Globally Unique Identifier (GUID) for the corresponding songin the music collection 26 and a reference to the song in the localstorage of the peer device 12. In addition, the registry entry mayinclude a Uniform Resource Locator (URL) or other network reference tothe song at a remote content source such as the subscription musicservice 18 or similar e-commerce service, a URL or other networkreference to a preview of the song at a remote content source such asthe subscription music service 18 or similar e-commerce service, a scoreof the song computed by the recommendation engine 24 as discussed below,and a status of the song, where the status may be an indicator ofdownload progress if the song is currently being downloaded. Inaddition, the registry 28 may store a registry entry for each songrecommended by recommendations from the other peer devices 14 and 16 oronly for ones of the recommended songs that the peer device 12 hasselected for download from the subscription music service 18.

In operation, each time a song is played by the music player 22, therecommendation engine 24 operates to provide a recommendationidentifying the song to the other peer devices 14, 16 via the P2Pnetwork. The recommendation does not include the song. In oneembodiment, the recommendation may be a recommendation file includingthe GUID for the song and optionally one or more URLs or other networkreferences enabling the other peer devices 14, 16 to obtain the song ora preview of the song from a remote content source such as thesubscription music service 18 or a similar e-commerce service. Therecommendation file may additionally or alternatively include a URLenabling the other peer devices 14, 16 to obtain the song from the peerdevice 12. In addition, as discussed below in detail, the recommendationengine 24 operates to programmatically, or automatically, select a nextsong to be played by the music player 22 based on recommendationsreceived from the other peer device 14, 16 identifying songs recentlyplayed by the other peer devices 14, 16 and user preferences associatedwith the user of the peer device 12. If the select song is not storedlocally at the peer device 12, the peer device 12 may obtain the selectsong or a preview thereof from, for example, the subscription musicservice 18, a similar e-commerce service, or another peer device 14, 16.

Like the peer device 12, the peer device 14 includes a music player 30,a recommendation engine 32, a music collection 34, and a registry 36;and the peer device 16 includes a music player 38, a recommendationengine 40, a music collection 42, and a registry 44.

The subscription music service 18 may be a service hosted by a serverconnected to the network 20. Exemplary subscription based music servicesthat may be modified to operate according to the present invention areYahoo! Music Unlimited digital music service and RealNetwork's Rhapsodydigital music service.

The system 10 may also include a content identification function 46 andan associated content descriptors database 48. The contentidentification function 46 and the content descriptors database 48 arehosted by a server connected to the network 20 and may be hosted by thesame server hosting the subscription music service 18. The contentidentification function 46 may be implemented in software, hardware, ora combination thereof. The content descriptors database 48 operates tostore a content descriptor for each of a number of songs known to thesystem 10. In one embodiment, the content descriptors database 48 storesa content descriptor for each song hosted by the subscription musicservice 18. Each content descriptor may include one or more digitalfingerprints for the associated song, the GUID for the song, metadatafor the song, and one or more URLs for the song and/or a preview of thesong at one or more remote content sources such as the subscriptionmusic service 18 or similar e-commerce service. The metadata for thesong may include, for example, the title of the song, artist, album,date of release, lyrics, an album cover image, and the like.

In operation, using the peer device 12 as an example, the contentidentification function 46 may be used by the peer device 12 to obtainGUIDs for the songs in the music collection 26. More specifically, thepeer device 12 may provide identification parameters for the songs inthe music collection 26 to the content identification function 46. Basedon a comparison of identification parameters for the songs and thecontent descriptors in the content descriptors database 48, the contentidentification function 46 identifies the songs and provides thecorresponding GUIDs and optionally the corresponding metadata and URL(s)to the peer device 12. The peer device 12 then uses this information togenerate corresponding registry entries for the songs in the registry28.

In addition to or as an alternative to the content identificationfunction 46, various schemes may be used to obtain or otherwise providethe GUIDs for the songs in the music collections 26, 34, and 42. Forexample, if the songs are downloaded from a remote content source suchas the subscription music service 18, the GUIDs may be provided alongwith the songs or be included as metadata within the song files. Asanother example, if the songs are imported from a Compact Disc (CD), theGUIDs for the songs may be obtained from a remote database as part ofthe importing process. More specifically, when importing the songs fromthe CD, information such as, for example, the number of tracks on the CDand the length of each track may be provided to a remote service such asGracenote (http://www.gracenote.com). In response, the remote servicemay provide the GUIDs and optionally metadata to the corresponding peerdevice 12, 14, or 16 for each of the songs imported from the CD.

FIG. 2 illustrates the registry 28 of the peer device 12 in more detailaccording to one embodiment of the present invention. Note that thisdiscussion is equally applicable to the registries 36 and 44 of theother peer devices 14 and 16. As illustrated, the registry 28 includes anumber of registry entries. In this example, the registry 28 includeseight registry entries. However, the present invention is not limitedthereto. Each of the registry entries includes a reference to acorresponding song. If the song is stored locally in the musiccollection 26, the reference is to the song in the local storage of thepeer device 12. If the song is not stored locally in the musiccollection, the reference may be a URL or other network reference to thesong and/or a URL or other network reference to a preview of the song atthe subscription music service 18 or similar content provider. In thisexample, ENTRY1-ENTRY3, ENTRY 5, and ENTRY 8 are registry entries forthe songs in the music collection 26 stored locally at the peer device12. ENTRY 4, ENTRY 5, and ENTRY 7 are entries for songs identified byrecommendations from the other peer devices 14 and 16 that reference thesongs or previews of the songs at the subscription music service 18.

It should be noted that in another embodiment, the registry entries forsongs not stored locally at the peer device 12 as part of the musiccollection 26 may additionally or alternatively include a reference suchas a URL to the corresponding song at one or more of the other peerdevices 14-16 such that the peer device 12 may download or stream thesong from one of the other peer devices 14-16 if desired.

FIG. 3 illustrates the operation of the content identification function46 according to one embodiment of the present invention. While thisexample focuses on the identification of multiple songs, the process isequally applicable to a single song. First, the peer device 12 discoversthe music collection 26 (step 300). For example, the peer device 12 mayscan the local storage of the peer device 12 or a select portion thereoffor songs. As another example, the peer device 12 may detect new songsdownloaded from a remote content source, imported from a CD, or thelike.

The peer device 12 then provides one or more identification parametersfor each of the songs to the content identification function 46 (step302). In a first embodiment, the identification parameters include oneor more digital fingerprints for each of the songs. In order to generatethe fingerprints, the peer device 12 may analyze one or more segments ofa song to determine, for example, beats-per-minute and/or compute a FastFourier Transform (FFT). The segments of the song analyzed to generatethe fingerprints may be selected at random or in some predeterminedfashion. For a more detailed discussion of generating fingerprints for asong and identifying the song based on the fingerprints, see U.S. Pat.No. 6,990,453, entitled SYSTEM AND METHODS FOR RECOGNIZING SOUND ANDMUSIC SIGNALS IN HIGH NOISE AND DISTORTION, issued Jan. 24, 2006, whichis hereby incorporated by reference in its entirety.

In a second embodiment, the identification parameters include one ormore samples of each of the songs, rather than fingerprints. The contentidentification function 46 may then generate fingerprints for the songsusing the samples of the songs. In a third embodiment, theidentification parameters include metadata describing each of the songsand optionally fingerprints for a select number of the songs. Thefingerprints of the select number of songs may be used for verificationpurposes in order to determine whether the peer device 12 is spoofingthe content identification function 46 with metadata for songs that arenot in fact stored in the music collection 26 of the peer device 12. Ina fourth embodiment, the identification parameters include metadatadescribing each of the songs and one or more samples of a select numberof the songs. The samples of the select number of songs may be used togenerate fingerprints for the songs, which may then be used forverification purposes as described above.

The content identification function 46 then identifies the songs usingthe song identification parameters and the content descriptors in thecontent descriptors database 48 (step 304). The manner in which thesongs are identified varies depending on the identification parameters.If the identification parameters are fingerprints for the songs, thecontent identification function 46 may identify the songs by comparingthe fingerprints to the fingerprints of the content descriptors in thecontent descriptors database 48. A song is identified when thefingerprints for the song match the fingerprints of a particular contentdescriptor. If the identification parameters are samples of the songs,the content identification function 46 may generate fingerprints for thesongs from the samples and then identify the songs by comparing thefingerprints to the fingerprints of the content descriptors. If theidentification parameters include metadata describing the songs, thecontent identification function 46 may identify the songs by comparingthe metadata to the metadata of the content descriptors. When themetadata of a song matches the metadata of a particular contentdescriptor, the song is identified as the song corresponding to thematching content descriptor.

Once the songs are identified, the GUIDs for the songs from thecorresponding content descriptors are provided to the peer device 12(step 306). Optionally, the metadata for the songs and the URLs forobtaining the songs and/or previews of the songs from a remote contentsource such as the subscription music service 18 or similar e-commerceservice may also be provided to the peer device 12. The peer device 12then generates registry entries for the songs in the registry 28 (step308). The registry entries include the GUIDs for the songs. In addition,the registry entries may include the metadata and/or URL(s) for thesongs. The metadata may additionally or alternatively be used to add,update, or correct metadata for the songs stored in the correspondingsong files.

FIG. 4 illustrates the operation of the peer device 12 with respect toproviding recommendations to the other peer devices 14-16 and receivingand processing recommendations from the other peer devices 14-16according to one embodiment of the present invention. The followingdiscussion is equally applicable to the other peer devices 14, 16.First, the peer devices 12-16 cooperate to establish a P2P network (step400). The P2P network may be initiated using, for example, an electronicor verbal invitation. Invitations may be desirable when the user wishesto establish the P2P network with a particular group of other users,such as his or her friends. Note that this may be beneficial when theuser desires that the music to which he or she listens be influencedonly by the songs listened to by, for example, the user's friends.Invitations may also be desirable when the number of peer devices withina local wireless coverage area of the peer device 12 is large. Asanother example, the peer device 12 may maintain a “buddy list”identifying friends of the user of the peer device 12, where the peerdevice 12 may automatically establish a P2P network with the peerdevices of the users identified by the “buddy list” when the peerdevices are within a local wireless coverage area of the peer device 12.

Alternatively, the peer device 12 may establish an ad-hoc P2P networkwith the other peer devices 14, 16 by detecting the other peer devices14, 16 within the local wireless coverage area of the peer device 12 andautomatically establishing the P2P network with at least a subset of thedetected peer devices 14, 16. In order to control the number of peerdevices within the ad-hoc P2P network, the peer device 12 may compareuser profiles of the users of the other peer devices 14, 16 with a userprofile of the user of the peer device 12 and determine whether topermit the other peer devices 14, 16 to enter the P2P network based onthe similarities of the user profiles. The user profiles may includeinformation identifying the users, demographic information, andoptionally statistical information regarding music collections of theusers, the play histories of the users, and the like.

At some point after the P2P network is established, the peer device 12plays a song (step 402). Initially, before any recommendations have beenreceived from the other peer devices 14, 16, the song may be a song fromthe music collection 26 selected by the user of the peer device 12.Prior to, during, or after playback of the song, the recommendationengine 24 sends a recommendation file identifying the song to the otherpeer devices 14, 16 (step 404). The recommendation file is also referredto herein as a recommendation. The recommendation file may include, butis not limited to, information identifying the song such as the GUID forthe song, title of the song, or the like; the URL for the song at aremote content source such as the subscription music service 18 or ane-commerce service enabling purchase and download of the song; a URLenabling download or streaming of a preview of the song from thesubscription music service 18 or a similar e-commerce service; metadatadescribing the song such as ID3 tags including, for example, genre, thetitle of the song, the artist of the song, the album on which the songcan be found, the date of release of the song or album, the lyrics, andthe like. In one embodiment, the recommendation file includes the GUIDfor the song and optionally a URL for the song at a remote contentsource such as the subscription music service 18 or similar e-commerceservice, a URL for a preview of the song at a remote content source suchas the subscription music service 18 or similar e-commerce service,and/or a URL for the song at the peer device 12.

The recommendation file may also include a list of recommendersincluding information identifying each user having previouslyrecommended the song and a timestamp for each recommendation. Forexample, if the song was originally played at the peer device 14 andthen played at the peer device 16 in response to a recommendation fromthe peer device 14, the list of recommenders may include informationidentifying the user of the peer device 14 or the peer device 14 and atimestamp identifying a time at which the song was played or recommendedby the peer device 14, and information identifying the user of the peerdevice 16 or the peer device 16 and a timestamp identifying a time atwhich the song was played or recommended by the peer device 16.Likewise, if the peer device 12 then selects the song for playback,information identifying the user of the peer device 12 or the peerdevice 12 and a corresponding timestamp may be appended to the list ofrecommenders.

The peer device 12, and more specifically the recommendation engine 24,also receives recommendations, or more specifically recommendationfiles, from the other peer devices 14, 16 (step 406). The recommendationfiles from the other peer devices 14, 16 identify songs played by theother peer devices 14, 16. In one embodiment, each of the recommendationfiles includes the GUID for the song and optionally a URL for the songat a remote content source such as the subscription music service 18 orsimilar e-commerce service, a URL for a preview of the song at a remotecontent source such as the subscription music service 18 or similare-commerce service, and/or a URL for the song at the peer device 14, 16.

Optionally, the recommendation engine 24 may filter the recommendationsfrom the other peer devices 14, 16 (step 408). More specifically, therecommendation engine 24 may filter the recommendations based on, forexample, user, genre, artist, title, album, lyrics, date of release, orthe like. In addition or alternatively, the recommendation engine 24 mayfilter the recommendations to remove or block recommendations for songsthat are not included in the music collection 26 or accessible to thepeer device 12 from a remote content source such as, for example, thesubscription music service 18 or similar e-commerce service. Songs maynot be accessible to the peer device 12 from the remote content sourceif, for example, the user of the peer device 12 is not registered withthe remote content source. Using the subscription music service 18 as anexample, songs hosted by the subscription music service 18 may not beaccessible to the peer device 12 unless the user of the peer device 12is a subscriber of the subscription music service 18. Note that if theuser thereafter registers with the remote content source, therecommendation engine 24 may provide previously blocked recommendationsif desired by the user. In another embodiment, the recommendation engine24 may filter the recommendations to remove or block recommendations forsongs that are not stored in the music collection 26.

Based on user preferences, the recommendation engine 24 thenautomatically selects a next song to play from the songs identified bythe recommendations received from the other peer devices 14, 16,optionally songs identified by previously received recommendations, andone or more songs from the music collection 26 (step 410). In thepreferred embodiment discussed below, the songs identified by therecommendations from the other peer devices 14, 16 and the songs fromthe music collection 26 are scored or ranked based on the userpreferences. Then, based on the scores, the recommendation engine 24selects the next song to play.

In one embodiment, the recommendation engine 24 considers only thosesongs identified by recommendations received since a previous songselection. For example, if the song played in step 402 was a songselected by the recommendation engine 24 based on prior recommendationsfrom the peer devices 14, 16, the recommendation engine 24 may onlyconsider the songs identified in new recommendations received after thesong was selected for playback in step 402 and may not consider thesongs identified in the prior recommendations. This may be beneficial ifthe complexity of the recommendation engine 24 is desired to be minimalsuch as when the peer device 12 is a mobile terminal or the like havinglimited processing and memory capabilities. In another embodiment, therecommendation engine 24 may consider all previously receivedrecommendations, where the recommendations may expire after apredetermined or user defined period of time.

As discussed below, the user preferences used to select the next song toplay may include a weight or priority assigned to each of a number ofcategories such as user, genre, decade of release, and availability.Generally, availability identifies whether songs are stored locally inthe music collection 26; available via the subscription music service18; available for download, and optionally purchase, from an e-commerceservice or one of the other peer devices 14, 16; or are not currentlyavailable. If not available, the user may choose to search for the songsif desired. The user preferences may be stored locally at the peerdevice 12 or obtained from a central server via the network 20. If thepeer device 12 is a portable device, the user preferences may beconfigured on an associated user system, such as a personal computer,and transferred to the peer device 12 during a synchronization process.The user preferences may alternatively be automatically provided orsuggested by the recommendation engine 24 based on a play history of thepeer device 12, the songs in the music collection 26, the user profilefor the peer device 12, or any combination thereof.

Once the next song to play is selected, the peer device 12 obtains theselected song (step 412). If the selected song is part of the musiccollection 26, the peer device 12 obtains the selected song from themusic collection 26. If the selected song is not part of the musiccollection 26, the recommendation engine 24 obtains the selected songfrom the subscription music service 18, an e-commerce service, or one ofthe other peer devices 14, 16. For example, as discussed above, therecommendation for the song may include a URL providing a link to thesong at a remote content source, and the peer device 12 may download oralternatively stream the selected song from the remote content sourceusing the URL. As another alternative, the recommendation for the songmay additionally or alternatively include a URL providing a link to thesong at one of the peer devices 14, 16 from which the correspondingrecommendation came such that the peer device 12 may download or streamthe selected song from the one of the peer devices 14, 16. Onceobtained, the selected song is played and the process repeats (steps402-412).

It should be noted that the recommendation engine 24 may provide adownload queue and operate to download the songs or previews of thesongs in the download queue using, for example, a background process.More specifically, as discussed below, both recommended songs andlocally stored songs may be scored by the recommendation engine 24. Forrecommended songs, the peer device 12 determines whether the songs arealready included in the music collection 26 by comparing, for example,the GUIDs of the recommended songs to the registry entries in theregistry 28. Recall that the registry includes a registry entry for eachsong in the music collection 26, where each registry entry includes theGUID for the corresponding song. Songs that are not stored locally andhave a score above a first threshold may be added to the download queuesuch that the songs are downloaded from, for example, the subscriptionmusic service 18. The GUIDs for the songs are obtained if needed, andregistry entries for the songs are generated and stored in the registry28 either before or after download from the subscription music service18. Songs that are not stored locally and have a score less than thefirst threshold but greater than a second threshold may be added to thedownload queue such that previews of the songs are downloaded from, forexample, the subscription music service 18.

FIG. 5 illustrates the operation of the peer devices 12-16 to providereal time song recommendations according to one embodiment of thepresent invention. The illustrated process is the same as discussedabove with respect to FIG. 4. As such, the details will not be repeated.In general, the peer devices 14, 16 play songs and, in response, providesong recommendations to the peer device 12 (steps 500-506). The peerdevice 12 may optionally filter the recommendations from the peerdevices 14, 16 (step 508). Based on user preferences of the user of thepeer device 12, the recommendation engine 24 of the peer device 12 thenautomatically selects the next song to play from the songs identified bythe recommendations, optionally songs identified by priorrecommendations from the peer devices 14, 16, and locally stored songsfrom the music collection 26 (step 510). The peer device 12 then obtainsand plays the selected song (steps 512-514). Either prior to, during, orafter playback of the selected song, the recommendation engine 24 of thepeer device 12 provides a recommendation identifying the selected songto the other peer devices 14, 16 (step 516-518).

FIG. 6 illustrates the system 10′ according to second embodiment of thepresent invention. In this embodiment, the peer devices 12′-16′ form aP2P network via the network 20 and a central server 50. The peer devices12′-16′ may be any device having a connection to the network 20 andaudio playback capabilities. For example, the peer devices 12′-16′ maybe personal computers, laptop computers, mobile telephones, portableaudio players, PDAs, or the like having either a wired or wirelessconnection to the network 20. As discussed above with respect to thepeer device 12, the peer device 12′ includes a music player 22′, arecommendation engine 24′, a music collection 26′, and a registry 28′.Likewise, the peer device 14′ includes a music player 30′, arecommendation engine 32′, a music collection 34′, and registry 36′; andthe peer device 16′ includes a music player 38′, a recommendation engine40′, a music collection 42′, and registry 44′.

The central server 50 includes a proxy function 52 and a user accountsdatabase 54. In this embodiment, the central server 50 may also includethe content identification function 46 and the content descriptorsdatabase 48. It should be noted that in an alternative embodiment, thesubscription music service 18, the proxy function 52, and the useraccounts database 54, and optionally the content identification function46 and the content descriptors database 48, may be hosted by the centralserver 50.

The proxy function 52 may be implemented in software, hardware, or acombination of software and hardware and operates as an intermediary fortransferring recommendations among the peer devices 12′-16′. Inaddition, the proxy function 52 may perform various functions. Forexample, as discussed below, the proxy function 52 may filterrecommendations, generate a list of songs in the music collections 26′,34′, and 42′ by monitoring the recommendations made by the peer devices12′-16′, and maintain a play history for the peer devices 12′-16′ basedon recommendations made by the peer devices 12′-16′.

The user accounts database 54 stores account information for the usersof the peer devices 12′-16′. Using the peer device 12′ as an example,the account information may include, for example, a user profile, a listof songs or at least a portion of the songs in the music collection 26′,a play history of the peer device 12′, information identifying thesubscription music service 18 or a number of similar services to whichthe user of the peer device 12′ is a subscriber, and a “buddy list” ofthe user. The “buddy list” identifies other users or other peer devices14′-16′ to which the recommendations of the peer device 12′ are to beprovided and from which the peer device 12′ is to receiverecommendations.

As discussed above with respect to FIGS. 1 and 2, the contentidentification function 46 and the content descriptors database 48 maybe used to obtain GUIDs for the songs in the music collections 26′, 34′,and 42′ of the peer devices 12′, 14′, and 16′. More specifically, usingthe peer device 12′ as an example, the peer device 12′ may provideidentification parameters for each of the songs in the music collection26′ to the central server 50, where the content identification function46 identifies the songs using the identification parameters and thecontent descriptors in the content descriptors database 48. The GUIDsfor the songs are then returned to the peer device 12′.

In addition to or as an alternative, the GUIDs for the songs in themusic collections 26′, 34′, and 42′ may be obtained in various othermanners. For example, if the songs are imported from a CD, the peerdevice 12′ may provide information regarding the tracks on the CD to aremote service in order to obtain the GUIDs as well as metadata for thetracks, or songs, imported from the CD. As another example, if the songsare downloaded from a remote content source such as the subscriptionmusic service 18, the GUIDs may be provided along with the downloadedsongs.

FIG. 7 illustrates the operation of the system 10′ of FIG. 6 accordingto one embodiment of the present invention. Prior to beginning theprocess, the peer devices 12′-16′ form a P2P network. Since the numberof peer devices 12′-16′ connected to the network 20 may be very large,the peer devices 12′-16′ may implement a technique for identifying adesired group of peer devices among which recommendations are to beshared. For example, the group of peers may be identified using, forexample, an electronic or verbal invitation. As another example, thepeer device 12′ may have an associated “buddy list” identifying friendsof the user of the peer device 12′, where the peer device 12′ mayautomatically establish a P2P network with the peer devices of the usersidentified by the “buddy list” when the peer devices are connected tothe network 20. Alternatively, the peer devices 12′-16′ may form anad-hoc network where the participants for the ad-hoc network areselected based on similarities in user profiles.

In this example, once the P2P network is established, the peer device14′ plays a song and, in response, provides a song recommendationidentifying the song to the proxy function 52 of the central server 50(step 600-602). Optionally, the proxy function 52 may process therecommendation to provide various functions (step 604). Using the peerdevice 12′ as an example, the proxy function 52 may determine whetherthe recommended song is included in the music collection 26′ of the peerdevice 12′ by comparing the GUID from the recommendation to a list ofsongs, or more specifically, the GUIDs for the songs, in the musiccollection 26′. The list of songs in the music collection 26′ may bestored in the user accounts database 54. The list of songs in the musiccollection 26′ may be generated by the content identification function46 as a result of the song identification process described above,generated by the proxy function 52 by monitoring recommendations fromthe peer device 12′, or a combination thereof. If the recommendation isfor a song that is not part of the music collection 26′ of the peerdevice 12′, the proxy function 52 may insert a URL for obtaining thesong or a preview of the song from the subscription music service 18 orsimilar e-commerce service.

The proxy function 52 may additionally or alternatively filterrecommendations based on various criteria. For example, the proxyfunction 52 may filter recommendations such that, for example, the peerdevice 12′ only receives recommendations for songs in the musiccollection 26′ or only receives recommendations for songs in the musiccollection 26′ or accessible to the peer device 12′ from a remotecontent source such as the subscription music service 18 or similare-commerce service with which the user of the peer device 12′ isregistered. Optionally, if the user of the peer device 12′ thereafterregisters with the remote content source, the proxy function 52 mayprovide previously blocked or removed recommendations to the peer device12′ if desired. The proxy function 52 may also filter therecommendations based on filtering criteria such as, for example, user,genre, artist, title, album, lyrics, date of release, or the like. Thefiltering criteria may be defined by the users of the peer devices12′-16′.

The proxy function 52 may also process the recommendation to determinewhether the corresponding song is hosted by a remote content source withwhich the user of the recipient device, which in this example is thepeer device 12′, is registered. This remote content source is referredto herein as a preferred source. The proxy function 52 may determinewhether the song is available from the preferred source. If so, a URLfor the song at the remote content source may be inserted into therecommendation. If not, the proxy function 52 may identify anotherremote content source from which the song is available and insertinformation enabling the user of the peer device 12′ to register withthe other remote content source in order to obtain the song. Inaddition, the proxy function 52 may insert a URL for the song at theother remote content source.

The proxy function 52 may also monitor the recommendations to generateand store a play history for the peer devices 12′-16′. Using the peerdevice 12′ as an example, the play history may include a list of GUIDsand time stamps corresponding to the recommendations received from thepeer device 12′ and therefore the songs played by the peer device 12′.

The proxy function 52 forwards the recommendation to the peer device 12′(steps 606). While not illustrated for clarity and ease of discussion,the proxy function 52 also sends the recommendation to other desiredrecipients, where in this example the peer device 16′ is another desiredrecipient. Note that the proxy function 52 may identify the peer devices12′ and 16′ to which the recommendation is to be forwarded using, forexample, the “buddy list” of the peer device 14′ stored in the useraccounts database 54. Alternatively, the recommendation from the peerdevice 14′ may identify the desired recipients of the recommendation,which in this example are the peer devices 12′ and 16′. As anotheralternative, if the number of peer devices connected to the network 20is relatively small, the proxy function 52 may forward therecommendation to all peer devices connected to the network 20.

Like the peer device 14′, the peer device 16′ also plays a song andsends a song recommendation to the peer device 12′ via the proxyfunction 52 of the central server 50 (steps 608-614). Again, while notillustrated for clarity, the recommendation for the song is alsoprovided to the peer device 14′ via the proxy function 52 of the centralserver 50.

From this point, the process continues as discussed above. Morespecifically, the recommendation engine 24′ may optionally filter therecommendations from the other peer devices 14′, 16′ (step 616). Notethat if the proxy function 52 has already filtered the recommendations,no filtering or limited filtering may be desired at the recommendationengine 24′. Based on user preferences, the recommendation engine 24′then automatically selects a next song to play from the songs identifiedby the recommendations received from the other peer devices 14′-16′,optionally songs identified by previously received recommendations fromthe peer devices 14′-16′, and one or more songs from the musiccollection 26′ (step 618). In the preferred embodiment discussed below,the songs identified by the recommendations from the other peer devices14′-16′ and the songs from the music collection 26′ are scored based onthe user preferences. Then, based on the scores, the recommendationengine 24′ selects the next song to play.

Once the next song to play is selected, the peer device 12′ obtains theselected song (step 620). If the selected song is part of the musiccollection 26′, the peer device 12′ obtains the selected song from themusic collection 26′. If the selected song is not part of the musiccollection 26′, the recommendation engine 24′ obtains the selected songfrom the subscription music service 18, an e-commerce service, or one ofthe other peer devices 14′-16′. For example, the selected song may beobtained from a source identified in the recommendation for the song.Note that in the one embodiment, the recommendations include the GUIDsof the corresponding songs. In order to determine whether therecommended songs are part of the music collection 26′ stored locally bythe peer device 12′, the peer device 12′ may compare the GUIDs from therecommendations to the GUIDs of the songs in the music collection 26′stored in the registry 28′. Once obtained, the selected song is played,and a recommendation for the song is provided to the other peer devices14′-16′ via the proxy function 52 of the central server 50 (steps622-630).

Returning to step 618, as discussed above, the recommendation engine 24′may provide a download queue for downloading songs or previews of songsusing a background process such that the songs or previews will beavailable when desired to be played. Songs having a score greater than afirst threshold that are not already in the music collection 26′ areadded to the download queue such that the songs are downloaded from aremote content source such as the subscription music service 18 orsimilar e-commerce service. Note that the recommendations may includeURLs for obtaining the songs from the remote content source. Registryentries for the songs may be generated and added to the registry 28′before, during, or after download. Songs having a score less than thefirst threshold but greater than a second threshold may be added to thedownload queue such that previews of the songs are downloaded from aremote content source such as the subscription music service 18 orsimilar e-commerce service. Again, the recommendations for the songs mayinclude URLs for obtaining the previews of the songs from the remotecontent source.

FIG. 8 illustrates the process of automatically selecting a song to playfrom the received recommendations and locally stored songs at the peerdevice 12′ according to one embodiment of the present invention.However, the following discussion is equally applicable to the peerdevices 12-16 of FIG. 1, as well as the other peer devices 14′-16′ ofFIG. 6. First, the user preferences for the user of the peer device 12′are obtained (step 700). The user preferences may include a weight orpriority assigned to each of a number of categories such as, but notlimited to, user, genre, decade of release, and availability. The userpreferences may be obtained from the user during an initialconfiguration of the recommendation engine 24′. In addition, the userpreferences may be updated by the user as desired. The user preferencesmay alternatively be suggested by the recommendation engine 24′ or thecentral server 50 based on a play history of the peer device 12′ and thesongs in the music collection 26′ of the peer device 12′. Note that theproxy function 52 of the central server 50 may ascertain the playhistory of the peer device 12′ by monitoring the recommendations fromthe peer device 12′ as the recommendations pass through the centralserver 50. The user preferences may be stored locally at the peer device12′ or remotely at the central server 50 in the user accounts database.If stored remotely, the recommendation engine 24′ may obtain the userpreferences from the central server 50 when desired.

Once recommendations are received from the other peer devices 14′-16′,the recommendation engine 24′ of the peer device 12′ scores the songsidentified by the recommendations based on the user preferences (step702). The recommendation engine 24′ also scores one or more local songsfrom the music collection 26′ (step 704). The recommendation engine 24′then selects the next song to play based, at least on part, on thescores of the recommended and local songs (step 706).

FIG. 9 illustrates an exemplary graphical user interface (GUI) 56 forconfiguring user preferences. First, the user assigns a weight tovarious categories. In this example, the categories are users, genre,decade, and availability. However, the present invention is not limitedthereto. The weights for the categories may be assigned alphabeticallyby selecting radio button 58, customized by the user by selecting radiobutton 60, or automatically suggested based on a user profile of theuser by selecting radio button 62. If alphabetical weighting isselected, the weights are assigned by alphabetically sorting thecategories and assigning a weight to each of the categories based on itsposition in the alphabetically sorted list of categories. As illustratedin FIG. 10, if customized weighting is selected, the user may bepresented with a GUI 64 for customizing the weighting of the categories.As illustrated in the exemplary embodiment of FIG. 10, the weights ofthe categories may be assigned by adjusting corresponding sliding bars66-72. Sliding bar 74 may be adjusted to assign a weight to a “no repeatfactor.” The no repeat factor is a dampening factor used to alter asong's score based on when the song was previously played at the peerdevice 12′ in order to prevent the same song from being continuallyrepeated.

Once the weights are assigned, the user may select an OK button 76 toreturn to the GUI 56 of FIG. 9 or select a REVERT button 78 to returnthe weights of the categories to their previous settings. In addition,the user may select a SUGGEST button 80 to have the recommendationengine 24′ or the central server 50 suggest weights for the categoriesbased on a user profile of the user, a play history for the peer device12′, the songs in the music collection 26′ of the peer device 12′, orthe like or any combination thereof. Note that the SUGGEST button 80 hasthe same effect as the radio button 62 of FIG. 9.

Returning to FIG. 9, radio buttons 82-86 are used to select a desiredmethod for assigning weights to each user in the P2P network, radiobuttons 88-92 are used to select a desired method for assigning weightsto each of a number of genres of music, radio buttons 94-98 are used toselect the desired method for assigning weights to each of a number ofdecades, and radio buttons 100-104 are used to select the desired methodfor assigning weights to a number of song availability types.

Regarding users, if the radio button 82 is selected, the users areassigned weights based on their respective positions in analphabetically sorted list of users. If the radio button 84 is selected,a GUI 106 (FIG. 11) is provided enabling the user to customize theweights assigned to a number of users from which recommendations arereceived. An exemplary embodiment of the GUI 106 is illustrated in FIG.11, where sliding bars 108-112 enable the user to assign customizedweights to corresponding users. Returning to FIG. 9, if the radio button86 is selected, the recommendation engine 24′ or the central server 50generates suggested weights for the users based on the user profiles forthe user and the user profile of the user associated with the peerdevice 12′, the play history of the user and the play history of theuser of the peer device 12′, the songs in the music collections of theuser and the songs in the music collection 26′ of the user of the peerdevice 12′, or the like or any combination thereof.

Regarding genres, if the radio button 88 is selected, the genres areassigned weights based on their respective positions in analphabetically sorted list of genres. If the radio button 90 isselected, a GUI 114 (FIG. 12) is provided enabling the user to customizethe weights assigned to a number of genres. An exemplary embodiment ofthe GUI 114 is illustrated in FIG. 12, where sliding bars 116-130 enablethe user to assign customized weights to corresponding genres. Returningto FIG. 9, if the radio button 92 is selected, the recommendation engine24′ or the central server 50 generates suggested weights for the genresbased on the user profile associated with the peer device 12′, the playhistory of the peer device 12′, the songs in the music collection 26′,or the like or any combination thereof.

Regarding decades, if the radio button 94 is selected, the decades areassigned weights based on their respective positions in achronologically sorted list of decades. If the radio button 96 isselected, a GUI 132 (FIG. 13) is provided enabling the user to customizethe weights assigned to a number of decades. An exemplary embodiment ofthe GUI 132 is illustrated in FIG. 13, where sliding bars 134-144 enablethe user to assign customized weights to corresponding decades.Returning to FIG. 9, if the radio button 98 is selected, therecommendation engine 24′ or the central server 50 generates suggestedweights for the decades based on the user profile associated with thepeer device 12′, the play history of the peer device 12′, the songs inthe music collection 26′, or the like or any combination thereof.

Regarding availability, if the radio button 100 is selected, theavailability types are assigned weights based on their respectivepositions in an alphabetically sorted list of availability types. If theradio button 102 is selected, a GUI 146 (FIG. 14) is provided enablingthe user to customize the weights assigned to a number of availabilitytypes. An exemplary embodiment of the GUI 146 is illustrated in FIG. 14,where sliding bars 148-154 enable the user to assign customized weightsto corresponding availability types. Returning to FIG. 9, if the radiobutton 104 is selected, the recommendation engine 24′ or the centralserver 50 generates suggested weights for the availability types basedon the user profile associated with the peer device 12′, whether theuser of the peer device 12′ has access to the subscription music service18, or the like or any combination thereof.

An exemplary equation for scoring a particular song is:Score=NRF·(WU·WUA+WG·WGA+WD·WDA+WA·WAA)·100,where NRF is the “no repeat factor”; WU is the weight assigned to theuser category; WUA is the weight assigned to the user attribute of thesong, which is the user recommending the song; WG is the weight assignedto the genre category; WGA is the weight assigned to the genre attributeof the song, which is the genre of the song; WD is the weight assignedto the decade category; WDA is the weight assigned to the decadeattribute of the song, which is the decade in which the song or thealbum associated with the song was released; WA is the weight assignedto the availability category; and WAA is the weight assigned to theavailability attribute of the song, which is the availability of thesong.

The NRF may, for example, be computed as:

${NRF} = {\frac{{MIN}\left( {{10 \cdot {NRFW}},{LASTREPEAT\_ INDEX}} \right)}{10 \cdot {NRFW}}.}$

As an example, assume that the following category weights have beenassigned:

User Category 1 Genre Category 7 Decade Category 7 Availability TypeCategory 5 NRFW 9Further assume that the attributes for the categories have been assignedweights as follows:

User Genre Decade Availability User A 5 Alternative 8 1950s 2 Local 8User B 5 Classic Rock 5 1960s 4 Subscription Network 2 User C 5 ArenaRock 5 1970s 7 Buy/Download 1 Jazz 5 1980s 9 Find 1 New Wave 2 1990s 5Punk 4 2000s 5 Dance 2 Country 2Thus, if a particular song to be scored is recommended by the user “UserC,” is from the “Alternative Genre,” is from the “1980s” decade, and isavailable from the subscription music service 18, the score of the songmay be computed as:

${Score} = {{NRF} \cdot \left( {{\frac{1}{20} \cdot \frac{5}{10}} + {\frac{7}{20} \cdot \frac{8}{10}} + {\frac{7}{20} \cdot \frac{9}{10}} + {\frac{5}{20} \cdot \frac{2}{10}}} \right) \cdot 100}$where if the song was last played 88 songs ago,

${NRF} = {\frac{{MIN}\left( {{10 \cdot 9},88} \right)}{10 \cdot 9} = {\frac{88}{90}.}}$Thus, the score for the song is

${Score} = {{\frac{88}{90} \cdot \left( {{\frac{1}{20} \cdot \frac{5}{10}} + {\frac{7}{20} \cdot \frac{8}{10}} + {\frac{7}{20} \cdot \frac{9}{10}} + {\frac{5}{20} \cdot \frac{2}{10}}} \right) \cdot 100} = {65.5.}}$

FIG. 15 is an exemplary GUI 156 showing a playlist for the peer device12′ including both local and recommended songs according to the presentinvention. However, note that a similar list may be maintainedinternally by the peer device 12 of FIG. 1 and potentially optimized todisplay at least a portion of the GUI 156 on the display of the peerdevice 12. In this example, both the local and recommended songs arescored, as described above, and sorted according to their scores. Inaddition, as illustrated in FIG. 16, the songs may be sorted based onanother criterion, which in the illustrated example is genre.

The GUI 156 may optionally allow the user to block songs havingparticular identified fields. In the examples of FIGS. 14 and 15, theuser has identified the genre “country” and the artist “iron maiden” asfields to be blocked, as illustrated by the underlining. The user mayselect fields to block by, for example, clicking on or otherwiseselecting the desired fields. Songs having the blocked fields are stillscored but are not obtained or played by the peer device 12′.

As discussed above, in one embodiment, the recommendation engine 24′ ofthe peer device 12′ may provide a download queue containing all songs tobe downloaded, and optionally purchased, from an external source such asthe subscription music service 18, an e-commerce service, or anotherpeer device 14′-16′. Songs in the download queue having scores above afirst predetermined or user defined threshold and previews of othersongs in the download queue having scores above a second predeterminedor user defined threshold but below the first threshold may beautomatically downloaded to the peer device 12′.

FIG. 17 illustrates the system 10′ of FIG. 6 according to anotherembodiment of the present invention. In general, the central server 50′of this embodiment additionally includes an augmentation function 158.Using the peer device 12′ as an example, the augmentation function 158generally operates to augment, or supplement, recommendations providedto the peer device 12′ when a recommendation level for the peer device12′ falls below a defined minimum recommendation level.

As illustrated, the system 10′ includes the peer devices 12′-16′ and anumber of additional peer devices 160-164. In this example, the peerdevices 12′-16′ form a P2P group 166 for exchanging recommendations inthe manner described above. The additional peer devices 160-164 are likethe peer devices 12′-16′ but are not part of the P2P group 166. Forexample, the additional peer devices 160-164 may not be included in the“buddy list” of the peer device 12′. As discussed below, in oneembodiment, the additional peer devices 160-164 may be used to augmentrecommendations provided to the peer devices 12′-16′.

Before describing the operation of the augmentation function 158, itshould be noted that minimum recommendation levels are defined for thepeer devices 12′-16′. As used herein, a “recommendation level” may bedefined as the number of active, online peer devices sendingrecommendations to a peer device. An active, online peer device is apeer device that is connected to the network 20 and actively playingsongs and sending recommendations. Thus, if the peer device 12′ isconnected to the network 20 but is not playing songs and therefore notsending recommendations, the peer device 12′ is not “active.”“Recommendation level” may alternatively be defined as a number ofrecommendations received by a peer device over a defined period of timesuch as, for example, a song selection period. A song selection periodis the period of time between the automatic selection of a song and theautomatic selection of the next song which is generally the duration ofthe selected song.

The minimum recommendation levels may be defined by the users of thepeer devices 12′-16′. Alternatively, the minimum recommendation levelsmay be defined by the central server 50′ based on the number of peerdevices in the P2P group 166. For example, if the “buddy list” of thepeer device 12′ includes only the peer devices 14′ and 16′, the minimumrecommendation level for the peer device 12′ may be defined as 2 or thenumber of recommendations normally provided by two peer devices.

The minimum recommendation levels for the peer devices 12′-16′ arestored in the user accounts database 54. More specifically, using thepeer device 12′ as an example, the user account for the user of the peerdevice 12′ may include the minimum recommendation level for the peerdevice 12′. The user account may also include a flag indicating whetherthe peer device 12′ or associated user account information such as theuser profile of the user of the peer device 12′, the user preferences ofthe user of the peer device 12′, the list of songs in the musiccollection 26′, and the play history of the peer device 12′ may be usedby the augmentation function 158 to augment recommendations for otherpeer devices such as the additional peer devices 160-164.

FIG. 18 illustrates the operation of the augmentation function 158according to one embodiment of the present invention. While thefollowing discussion uses the peer device 12′ as an example, thediscussion is equally applicable to the other peer devices 14′-16′ and160-164. The augmentation function 158 monitors the recommendation levelfor the peer device 12′ to detect when the recommendation level for thepeer device 12′ falls below the minimum recommendation level for thepeer device 12′ (step 800). In one embodiment, the recommendation levelis defined as the number of active, online peers from which the peerdevice 12′ is receiving recommendations. Thus, the augmentation function158 may monitor the recommendation level of the peer device 12′ bymonitoring a status of each of the peer devices 14′, 16′. As usedherein, the status of, for example, the peer device 14′ indicateswhether the peer device 14′ is connected to the network 20, or online,and whether the peer device 14′ is actively sending recommendations tothe central server 50′. Normally, if both of the peer devices 14′ and16′ in the P2P group 166 are online and sending recommendations, therecommendation level for the peer device 12′ is 2. Thus, if one of thepeer devices 14′, 16′ is offline or inactive, then the recommendationlevel for the peer device 12′ is 1. If the peer device 14′ is offline orinactive and the peer device 16′ is offline or inactive, then therecommendation level for the peer device 12′ is 0. Assuming that theminimum recommendation level for the peer device 12′ is 2, theaugmentation function 158 detects that the recommendation level for thepeer device 12′ has fallen below the minimum recommendation level whenthe peer device 14′ is offline or inactive, when the peer device 16′ isoffline or inactive, or when the peer device 14′ and the peer device 16′are both offline or inactive.

In another embodiment, the recommendation level is defined as the numberof recommendations provided to the peer device 12′ over a defined periodof time. The defined period of time may be, for example, a songselection period substantially corresponding to a period of time betweenselecting songs to play at the peer device 12′. In this example, sincethe peer device 12′ normally receives recommendations from the two peerdevices 14′ and 16′, the minimum recommendation level may be defined astwo recommendations per song selection period. The augmentation function158 detects that the recommendation level for the peer device 12′ hasfallen below the minimum recommendation level when the number ofrecommendations over the defined period of time is less than the minimumrecommendation level.

In response to detecting that the recommendation level for the peerdevice 12′ is less than the minimum recommendation level for the peerdevice 12′, the augmentation function 158 augments the recommendationsprovided to the peer device 12′ with additional recommendations suchthat the recommendation level for the peer device 12′ is increased to orabove the minimum recommendation level for the peer device 12′ (step802). The manner in which the augmentation function 158 augments therecommendations for the peer device 12′ vary depending on the particularimplementation.

In a first embodiment, the augmentation function 158 selects one or moreof the additional peer devices 160-164 that is online and active anduses the recommendations from the one or more selected peer devices toaugment the recommendations provided to the peer device 12′. Morespecifically, if the peer device 16′ is offline or inactive, theaugmentation function 158 may desire to select one of the additionalpeer devices 160-164 for augmenting the recommendations from theremaining peer device 14′ to provide augmented recommendations to thepeer device 12′ at or above the minimum recommendation level. Theaugmentation function 158 may select one of the additional peer devices160-164 based on a comparison of the user profiles of the users of theadditional peer devices 160-164, the music collections of the additionalpeer devices 160-164, the play histories of the additional peer devices160-164, the user preferences of the users of the additional peerdevices 160-164, or any combination thereof to the correspondinginformation for the peer device 12′ or alternatively the correspondinginformation for the peer devices 16′ that is offline or inactive.

In a second embodiment, the augmentation function 158 augments therecommendations for the peer device 12′ using the play histories of theoffline or inactive peer devices in the group 166. For example, if thepeer device 16′ is offline or inactive, the augmentation function 158may use the play history of the peer device 16′ to provide additionalrecommendations. The additional recommendations may then be used toaugment, or supplement, the recommendations from the peer device 14′ toprovide augmented recommendations at or above the minimum recommendationlevel for the peer device 12′.

In a third embodiment, the augmentation function 158 augments therecommendations for the peer device 12′ by generating additionalrecommendations based on the user profiles of the offline or inactivepeer devices in the P2P group 166. The user profile used to generate theadditional recommendations may include information identifying the userof the peer device 16′, demographic information regarding the user ofthe peer device 16′, and statistical information describing the musiccollection 42′ of the peer device 16′ and/or the songs in the playhistory of the peer device 16′. The statistical information describingthe music collection 42′ of the peer device 16′ may include, forexample, a genre distribution, an artist distribution, a release yeardistribution, or the like or any combination thereof. The genredistribution may be generated by computing, for each genre, the numberof songs in the music collection 42′ in that genre divided by a totalnumber of songs in the music collection 42′. The artist distribution andrelease year distributions may be generated in using a similaralgorithm.

For example, if the peer device 16′ is offline or inactive, theaugmentation function 158 may generate additional recommendations basedon the user profile for the peer device 16′. As will be apparent to oneof ordinary skill in the art, a number of recommendation algorithms forrecommending songs based on a user profile are known and are not thesubject of this specification. Any applicable recommendation algorithmmay be used to generate the additional recommendations based on the userprofile for the peer device 16′. In operation, the augmentation function158 may recommend a number of songs from, for example, a favorite genreof the user of the peer device 16′, where the favorite genre may beidentified using the user profile. The additional recommendations areused to supplement the recommendations from the peer device 14′ toprovide augmented recommendations to the peer device 12′ at a levelequal to or greater than the minimum recommendation level for the peerdevice 12′.

In a fourth embodiment, the augmentation function 158 augments therecommendations for the peer device 12′ by generating additionalrecommendations based on a user profile for the peer device 12′. Anyapplicable recommendation algorithm may be used to generate theadditional recommendations based on the user profile for the peer device16′. In operation, if, for example, the peer device 16′ is offline orinactive, the augmentation function 158 generates a number of additionalrecommendations for songs from, for example, a favorite genre of theuser of the peer device 12′, where the favorite genre may be identifiedusing the user profile. The additional recommendations are used tosupplement the recommendations from the peer device 14′ to provideaugmented recommendations to the peer device 12′ at a level equal to orgreater than the minimum recommendation level for the peer device 12′.

Note that while the four different augmentation schemes for theaugmentation function 158 are separately described above, theaugmentation function 158 may use any combination of the fouraugmentation schemes. Further, the four augmentation schemes describedabove are exemplary and are not intended to limit the scope of thepresent invention.

FIG. 19 is a block diagram of an exemplary embodiment of the peer device12 of FIG. 1. However, the following discussion is equally applicable tothe other peer devices 14, 16. In general, the peer device 12 includes acontrol system 168 having associated memory 170. In this example, themusic player 22 and the recommendation engine 24 are at least partiallyimplemented in software and stored in the memory 170. The peer device 12also includes a storage unit 172 operating to store the music collection26 (FIG. 1). The storage unit 172 may be any number of digital storagedevices such as, for example, one or more hard-disc drives, one or morememory cards, RAM, one or more external digital storage devices, or thelike. The music collection 26 may alternatively be stored in the memory170. The peer device 12 also includes a communication interface 174. Thecommunication interface 174 includes a local wireless communicationinterface for establishing the P2P network with the other peer devices14, 16. The local wireless interface may operate according to, forexample, one of the suite of IEEE 802.11 standards, the Bluetoothstandard, or the like. The communication interface 174 may also includea network interface communicatively coupling the peer device 12 to thenetwork 20 (FIG. 1). The peer device 12 also includes a user interface176, which may include components such as a display, speakers, a userinput device, and the like.

FIG. 20 is a block diagram of an exemplary embodiment of the peer device12′ of FIG. 6. However, the following discussion is equally applicableto the other peer devices 14′-16′. In general, the peer device 12′includes a control system 178 having associated memory 180. In thisexample, the music player 22′ and the recommendation engine 24′ are atleast partially implemented in software and stored in the memory 180.The peer device 12′ also includes a storage unit 182 operating to storethe music collection 26′ (FIG. 6). The storage unit 182 may be anynumber of digital storage devices such as, for example, one or morehard-disc drives, one or more memory cards, RAM, one or more externaldigital storage devices, or the like. The music collection 26′ mayalternatively be stored in the memory 180. The peer device 12′ alsoincludes a communication interface 184. The communication interface 184includes a network interface communicatively coupling the peer device12′ to the network 20 (FIG. 6). The peer device 12′ also includes a userinterface 186, which may include components such as a display, speakers,a user input device, and the like.

FIG. 21 is a block diagram of an exemplary embodiment of the centralserver 50 of FIG. 6. In general, the central server 50 includes acontrol system 188 having associated memory 190. In this example, thecontent identification function 46 and the proxy function 52 are atleast partially implemented in software and stored in the memory 190.The central server 50 also includes a storage unit 192 operating tostore, for example, the content descriptors database 48 and the useraccounts database 54 (FIG. 6). The storage unit 192 may be any number ofdigital storage devices such as, for example, one or more hard-discdrives, one or more memory cards, RAM, one or more external digitalstorage devices, or the like. The central server 50 also includes acommunication interface 194. The communication interface 194 includes anetwork interface communicatively coupling the central server 50 to thenetwork 20 (FIG. 6). The central server 50 may also include a userinterface 196, which may include components such as a display, speakers,a user input device, and the like.

FIG. 22 is a block diagram of an exemplary embodiment of the centralserver 50′ of FIG. 17. In general, the central server 50′ includes acontrol system 198 having associated memory 200. In this example, thecontent identification function 46, the proxy function 52, and theaugmentation function 158 are at least partially implemented in softwareand stored in the memory 200. The central server 50′ also includes astorage unit 202 operating to store, for example, the contentdescriptors database 48 and the user accounts database 54 (FIG. 17). Thestorage unit 202 may be any number of digital storage devices such as,for example, one or more hard-disc drives, one or more memory cards,RAM, one or more external digital storage devices, or the like. Thecentral server 50′ also includes a communication interface 204. Thecommunication interface 204 includes a network interface communicativelycoupling the central server 50′ to the network 20 (FIG. 17). The centralserver 50′ may also include a user interface 206, which may includecomponents such as a display, speakers, a user input device, and thelike.

The present invention provides substantial opportunity for variationwithout departing from the spirit or scope of the present invention. Forexample, while FIG. 1 illustrates the peer devices 12-16 forming the P2Pnetwork via local wireless communication and FIG. 6 illustrates the peerdevices 12′-16′ forming the P2P network via the network 20, the presentinvention is not limited to either a local wireless P2P network or aWide Area Network (WAN) P2P network in the alternative. Morespecifically, a particular peer device, such as the peer device 12, mayform a P2P network with other peer devices using both local wirelesscommunication and the network 20. Thus, for example, the peer device 12may receive recommendations from both the peer devices 14, 16 (FIG. 1)via local wireless communication and from the peer devices 14′-16′ (FIG.6) via the network 20.

As another example, while the discussion herein focuses on songrecommendations, the present invention is not limited thereto. Thepresent invention is equally applicable to recommendations for othertypes of media presentations such as video presentations. Thus, thepresent invention may additionally or alternatively provide movierecommendations, television program recommendations, or the like.

Further, with respect to the play histories of the peer devices 12′-16′ascertained and stored by the central server 50, the central server 50may provide additional services using the play histories. For example,the user of the peer device 12′ may desire to listen to the songs thathe previously listened to at some point in time. For example, the usermay desire to listen to the same songs that he listened to one year ago.If so, the peer device 12′-16′ may request the play history for the peerdevice 12′ or another peer device associated with the user from one yearago. As another example, the user may desire to listen to, for example,the same songs that a friend or celebrity listened to at some time inthe past. For example, the user may desire to listen to the songs that afamous musician was listening to six months ago. The central server 50may provide the desired play history in the form of, for example, theGUIDs for corresponding songs and optionally URLs for obtaining thesongs from a remote content source such as the subscription musicservice 18 or similar e-commerce service. The peer device 12′ may thenobtain ones of the songs not in the music collection 26′ from the remotecontent source.

Those skilled in the art will recognize improvements and modificationsto the preferred embodiments of the present invention. All suchimprovements and modifications are considered within the scope of theconcepts disclosed herein and the claims that follow.

What is claimed is:
 1. A central server comprising: a communicationinterface communicatively coupling the central server to a plurality ofpeer devices via a network; and a control system associated with thecommunication interface and comprising: a proxy function adapted to:receive media recommendations from ones of a group of peer devices thatare active and online as media presentations identified by the mediarecommendations are played by the ones of the group of peer devices, thegroup of peer devices comprising at least one of the plurality of peerdevices associated with a first one of the plurality of peer devices;and provide the media recommendations to the first one of the pluralityof peer devices; and an augmentation function adapted to: monitor amedia recommendation level for the first one of the plurality of peerdevices wherein in order to monitor the media recommendation level, theaugmentation function is further adapted to monitor a number of mediarecommendations provided to the first one of the plurality of peerdevices over a defined period of time; determine whether the mediarecommendation level for the first one of the plurality of peer devicesis less than a minimum recommendation level; and augment the mediarecommendations provided to the first one of the plurality of peerdevices if the media recommendation level is less than the minimumrecommendation level.
 2. The central server of claim 1 wherein the mediarecommendation level is indicative of a number of media recommendationsprovided to the first one of the plurality of peer devices.
 3. Thecentral server of claim 1 wherein in order to monitor the mediarecommendation level, the augmentation function is further adapted todetermine whether each of the group of peer devices associated with thefirst one of the plurality of peer devices is active and online.
 4. Thecentral server of claim 3 wherein the minimum recommendation level is aminimum number of peer devices from the group of peer devices that areto be active and online, and in order to determine whether the mediarecommendation level for the first one of the plurality of peer devicesis less than the minimum recommendation level, the augmentation functionis further adapted to determine whether a number of the group of peerdevices that are active and online is less than the minimum number ofpeer devices.
 5. The central server of claim 1 wherein the minimumrecommendation level is a minimum number of media recommendations, andin order to determine whether the media recommendation level for thefirst one of the plurality of peer devices is less than the minimumrecommendation level, the augmentation function is further adapted todetermine whether the number of media recommendations provided to thefirst one of the plurality of peer devices over the defined period oftime is less than the minimum number of media recommendations.
 6. Thecentral server of claim 1 wherein in order to augment the mediarecommendations, the augmentation function is further adapted to: selectat least one additional peer device that is active and online from theplurality of peer devices; receive additional media recommendations fromthe at least one additional peer device as media presentationsidentified by the additional media recommendations are played by the atleast one additional peer device; and augment the media recommendationsprovided to the first one of the plurality of peer devices with theadditional media recommendations.
 7. The central server of claim 1wherein in order to augment the media recommendations, the augmentationfunction is further adapted to: generate additional mediarecommendations based on a play history of at least one of the group ofpeer devices that is at least one of a group consisting of: inactive andoffline; and augment the media recommendations provided to the first oneof the plurality of peer devices with the additional mediarecommendations.
 8. The central server of claim 1 wherein in order toaugment the media recommendations, the augmentation function is furtheradapted to: generate additional media recommendations based on a userprofile associated with at least one of the group of peer devices thatis at least one of a group consisting of: inactive and offline; andaugment the media recommendations provided to the first one of theplurality of peer devices with the additional media recommendations. 9.The central server of claim 1 wherein in order to augment the mediarecommendations, the augmentation function is further adapted to:generate additional media recommendations based on a user profile of auser of the first one of the plurality of peer devices; and augment themedia recommendations provided to the first one of the plurality of peerdevices with the additional media recommendations.
 10. A method ofoperation of a computing device operating in a peer-to-peer mediarecommendation system, comprising: receiving media recommendations fromones of a group of peer devices that are active and online as mediapresentations identified by the media recommendations are played by theones of the group of peer devices, the group of peer devices comprisingat least one of a plurality of peer devices associated with a first oneof the plurality of peer devices; providing the media recommendations tothe first one of the plurality of peer devices; monitoring a mediarecommendation level for the first one of the plurality of peer deviceswherein monitoring the media recommendation level includes monitoring anumber of media recommendations provided to the first one of theplurality of peer devices over a defined period of time; determiningwhether the media recommendation level for the first one of theplurality of peer devices is less than a minimum recommendation level;and augmenting the media recommendations provided to the first one ofthe plurality of peer devices if the media recommendation level is lessthan the minimum recommendation level.
 11. The method of claim 10wherein the media recommendation level is indicative of a number ofmedia recommendations provided to the first one of the plurality of peerdevices.
 12. The method of claim 10 wherein monitoring the mediarecommendation level comprises determining whether each of the group ofpeer devices associated with the first one of the plurality of peerdevices is active and online.
 13. The method of claim 12 wherein theminimum recommendation level is a minimum number of peer devices fromthe group of peer devices that are to be active and online, anddetermining whether the media recommendation level for the first one ofthe plurality of peer devices is less than the minimum recommendationlevel comprises determining whether a number of the group of peerdevices that are active and online is less than the minimum number ofpeer devices.
 14. The method of claim 10 wherein monitoring the mediarecommendation level comprises monitoring a number of mediarecommendations provided to the first of the plurality of peer devicesover a defined period of time.
 15. The method of claim 14 wherein theminimum recommendation level is a minimum number of mediarecommendations, and determining whether the media recommendation levelfor the first one of the plurality of peer devices is less than theminimum recommendation level comprises determining whether the number ofmedia recommendations provided to the first of the plurality of peerdevices over the defined period of time is less than the minimum numberof media recommendations.
 16. The method of claim 10 wherein augmentingthe media recommendations comprises: selecting at least one additionalpeer device that is active and online from the plurality of peerdevices; receiving additional media recommendations from the at leastone additional peer device as media presentations identified by theadditional media recommendations are played by the at least oneadditional peer device; and augmenting the media recommendationsprovided to the first one of the plurality of peer devices with theadditional media recommendations.
 17. The method of claim 10 whereinaugmenting the media recommendations comprises: generating additionalmedia recommendations based on a play history of at least one of thegroup of peer devices that is at least one of a group consisting of:inactive and offline; and augmenting the media recommendations providedto the first one of the plurality of peer devices with the additionalmedia recommendations.
 18. The method of claim 10 wherein augmenting themedia recommendations comprises: generating additional mediarecommendations based on a user profile associated with at least one ofthe group of peer devices that is at least one of the group consistingof: inactive and offline; and augmenting the media recommendationsprovided to the first one of the plurality of peer devices with theadditional media recommendations.
 19. The method of claim 10 whereinaugmenting the media recommendations comprises: generating additionalmedia recommendations based on a user profile of a user of the first oneof the plurality of peer devices; and augmenting the mediarecommendations provided to the first one of the plurality of peerdevices with the additional media recommendations.